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AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts by Jiang, Meng; Yu, Yi Jing; Zhao, Qing; Li, Jianqiang; Song, Changwei; Qi, Hongzhi; Zhai, Wei; Luo, Dan; Wang, Xiaoqin; Fu, Guanghui; Yang, Bing Xiang is a scholarly article available to read on EtoBox.

What is AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts about?

Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care. In current society, individuals frequently express negative emotions on social media on specific topics, often exhibiting cognitive distortions, including suicidal behaviors in extreme cases. Yet, there is a notable absence of methodologies for analyzing cognitive pathways that could aid psychotherapists in conducting effective interventions online. In this study, we gathered data from social media and established the task of extracting cognitive pathways, annotating the data based on a cognitive theoretical framework. We initially categorized the task of extracting cognitive pathways as a hierarchical text classification with four main categories and nineteen subcategories. Following this, we structured a text summarization task to help psychotherapists quickly grasp the essential information. Our experiments evaluate the performance of deep learning and large language models (LLMs) on these tasks. The results demonstrate that our deep lear

Author

Jiang, Meng; Yu, Yi Jing; Zhao, Qing; Li, Jianqiang; Song, Changwei; Qi, Hongzhi; Zhai, Wei; Luo, Dan; Wang, Xiaoqin; Fu, Guanghui; Yang, Bing Xiang

Published

2024

Language

EN